Speech recognition system using enhanced mel frequency cepstral coefficient with windowing and framing method

2017 ◽  
Vol 22 (S5) ◽  
pp. 11669-11679 ◽  
Author(s):  
S. Lokesh ◽  
M. Ramya Devi
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Mel Frequency Cepstral Coefficient

scholarly journals Continuous Speech Recognition System for Kannada Language with Triphone Modelling using HTK

2019 ◽  
Vol 8 (3) ◽  
pp. 7827-7831
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Experimental Result ◽  
Speech Recognition System ◽  
Continuous Speech Recognition ◽  
Frequency Scale ◽  
Context Dependent ◽  
Mel Frequency Cepstral Coefficient

Kannada is the regional language of India spoken in Karnataka. This paper presents development of continuous kannada speech recognition system using monophone modelling and triphone modelling using HTK. Mel Frequency Cepstral Coefficient (MFCC) is used as feature extractor, exploits cepstral and perceptual frequency scale leads good recognition accuracy. Hidden Markov Model is used as classifier. In this paper Gaussian mixture splitting is done that captures the variations of the phones. The paper presents performance of continuous Kannada Automatic Speech Recognition (ASR) system with respect to 2, 4,8,16 and 32 Gaussian mixtures with monophone and context dependent tri-phone modelling. The experimental result shows that good recognition accuracy is achieved for context dependent tri-phone modelling than monophone modelling as the number Gaussian mixture is increased.

scholarly journals Developing Speech Recognition System for Quranic Verse Recitation Learning Software

2012 ◽  
Vol 1 (2) ◽  
pp. 14
Author(s):  
Budiman Putra ◽  
B. Atmaja ◽  
D. Prananto
Keyword(s):  
Speech Recognition ◽  
Learning Process ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Speech Recognition System ◽  
Multimedia Software ◽  
Experiment Data ◽  
Learning Flexibility ◽  
Learning Software ◽  
Mel Frequency Cepstral Coefficient

Quran as holy book for Muslim consists of many rules which are needed to be considered in reading Quran verse properly. If the recitation does not meet all of those rules, the meaning of Quran verse recited will be different with its origins. Intensive learning is needed to be able to do correct recitation. However, the limitation of teachers and time to study Quran verse recitation together in a class could be an obstacle in Quran recitation learning. In order to minimize the obstacle and to ease the learning process we implement speech recognition techniques based on Mel Frequency Cepstral Coefficient (MFCC) features and Gaussian Mixture Model (GMM) modeling, we have successfully designed and developed Quran verse recitation learning software in prototype stage. This software is interactive multimedia software which has many features for learning flexibility and effectiveness. This paper explains the developing of speech recognition system for Quran learning software which is built with the ability to perform evaluation and correction in Quran recitation. In this paper, the authors present clearly the built and tested prototype of the system based on experiment data.

scholarly journals Direct Recovery of Clean Speech Using a Hybrid Noise Suppression Algorithm for Robust Speech Recognition System

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Peng Dai ◽  
Ing Yann Soon ◽  
Rui Tao
Keyword(s):  
Speech Recognition ◽  
Noise Suppression ◽  
Recognition Rate ◽  
Nonlinear Function ◽  
Recognition System ◽  
Speech Recognition System ◽  
Direct Solution ◽  
Power Domain ◽  
Discontinuity Problem ◽  
Mel Frequency Cepstral Coefficient

A new log-power domain feature enhancement algorithm named NLPS is developed. It consists of two parts, direct solution of nonlinear system model and log-power subtraction. In contrast to other methods, the proposed algorithm does not need prior speech/noise statistical model. Instead, it works by direct solution of the nonlinear function derived from the speech recognition system. Separate steps are utilized to refine the accuracy of estimated cepstrum by log-power subtraction, which is the second part of the proposed algorithm. The proposed algorithm manages to solve the speech probability distribution function (PDF) discontinuity problem caused by traditional spectral subtraction series algorithms. The effectiveness of the proposed filter is extensively compared using the standard database, AURORA2. The results show that significant improvement can be achieved by incorporating the proposed algorithm. The proposed algorithm reaches a recognition rate of over 86% for noisy speech (average from SNR 0 dB to 20 dB), which means a 48% error reduction over the baseline Mel-frequency Cepstral Coefficient (MFCC) system.

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

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